Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations28732
Missing cells154
Missing cells (%)< 0.1%
Duplicate rows13926
Duplicate rows (%)48.5%
Total size in memory34.9 MiB
Average record size in memory1.2 KiB

Variable types

Text13
Categorical7
Numeric8

Alerts

Dataset has 13926 (48.5%) duplicate rowsDuplicates
Autoridad Ambiental is highly overall correlated with Código del Departamento and 5 other fieldsHigh correlation
Código del Departamento is highly overall correlated with Autoridad Ambiental and 2 other fieldsHigh correlation
Código del Municipio is highly overall correlated with Autoridad Ambiental and 2 other fieldsHigh correlation
Días de excedencias is highly overall correlated with Excedencias limite actual and 1 other fieldsHigh correlation
Excedencias limite actual is highly overall correlated with Días de excedencias and 1 other fieldsHigh correlation
Latitud is highly overall correlated with Autoridad Ambiental and 1 other fieldsHigh correlation
Longitud is highly overall correlated with Autoridad Ambiental and 1 other fieldsHigh correlation
Nombre del Departamento is highly overall correlated with Autoridad Ambiental and 5 other fieldsHigh correlation
Porcentaje excedencias limite actual is highly overall correlated with Días de excedencias and 1 other fieldsHigh correlation
Tiempo de exposición (horas) is highly overall correlated with VariableHigh correlation
Tipo de Estación is highly overall correlated with Autoridad Ambiental and 1 other fieldsHigh correlation
Unidades is highly overall correlated with VariableHigh correlation
Variable is highly overall correlated with Tiempo de exposición (horas) and 1 other fieldsHigh correlation
Tipo de Estación is highly imbalanced (52.2%)Imbalance
Excedencias limite actual has 23462 (81.7%) zerosZeros
Porcentaje excedencias limite actual has 23464 (81.7%) zerosZeros
Días de excedencias has 23464 (81.7%) zerosZeros

Reproduction

Analysis started2025-11-09 03:50:59.013413
Analysis finished2025-11-09 03:51:06.181661
Duration7.17 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct501
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-11-08T22:51:06.354006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.615655
Min length5

Characters and Unicode

Total characters161349
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row9,020
2nd row9,020
3rd row9,020
4th row9,020
5th row9,020
ValueCountFrequency (%)
99,9991176
 
4.1%
8,214528
 
1.8%
8,213526
 
1.8%
8,219500
 
1.7%
8,208481
 
1.7%
8,218474
 
1.6%
8,211466
 
1.6%
8,216458
 
1.6%
28,202418
 
1.5%
9,023403
 
1.4%
Other values (491)23302
81.1%
2025-11-08T22:51:06.620054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
,28732
17.8%
120518
12.7%
819867
12.3%
318985
11.8%
218449
11.4%
917421
10.8%
014093
8.7%
56731
 
4.2%
45689
 
3.5%
65446
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)161349
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
,28732
17.8%
120518
12.7%
819867
12.3%
318985
11.8%
218449
11.4%
917421
10.8%
014093
8.7%
56731
 
4.2%
45689
 
3.5%
65446
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)161349
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
,28732
17.8%
120518
12.7%
819867
12.3%
318985
11.8%
218449
11.4%
917421
10.8%
014093
8.7%
56731
 
4.2%
45689
 
3.5%
65446
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)161349
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
,28732
17.8%
120518
12.7%
819867
12.3%
318985
11.8%
218449
11.4%
917421
10.8%
014093
8.7%
56731
 
4.2%
45689
 
3.5%
65446
 
3.4%

Autoridad Ambiental
Categorical

High correlation 

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
SDA
6042 
AMVA
5703 
CORANTIOQUIA
2402 
DAGMA
2028 
CAR
1931 
Other values (25)
10626 

Length

Max length23
Median length16
Mean length6.0719407
Min length3

Characters and Unicode

Total characters174459
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAMVA
2nd rowAMVA
3rd rowAMVA
4th rowAMVA
5th rowAMVA

Common Values

ValueCountFrequency (%)
SDA6042
21.0%
AMVA5703
19.8%
CORANTIOQUIA2402
 
8.4%
DAGMA2028
 
7.1%
CAR1931
 
6.7%
CORPOBOYACA1521
 
5.3%
CDMB1418
 
4.9%
CORPOCESAR1072
 
3.7%
CORPAMAG1016
 
3.5%
CVC908
 
3.2%
Other values (20)4691
16.3%

Length

2025-11-08T22:51:06.710871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sda6042
20.2%
amva5703
19.1%
corantioquia2402
 
8.0%
dagma2028
 
6.8%
car1987
 
6.6%
corpoboyaca1521
 
5.1%
cdmb1418
 
4.7%
corpocesar1072
 
3.6%
corpamag1016
 
3.4%
epa981
 
3.3%
Other values (22)5767
19.3%

Most occurring characters

ValueCountFrequency (%)
A43027
24.7%
C18279
10.5%
O16411
 
9.4%
R15778
 
9.0%
M11309
 
6.5%
D10048
 
5.8%
S7510
 
4.3%
V6799
 
3.9%
I6678
 
3.8%
P5795
 
3.3%
Other values (20)32825
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)174459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A43027
24.7%
C18279
10.5%
O16411
 
9.4%
R15778
 
9.0%
M11309
 
6.5%
D10048
 
5.8%
S7510
 
4.3%
V6799
 
3.9%
I6678
 
3.8%
P5795
 
3.3%
Other values (20)32825
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)174459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A43027
24.7%
C18279
10.5%
O16411
 
9.4%
R15778
 
9.0%
M11309
 
6.5%
D10048
 
5.8%
S7510
 
4.3%
V6799
 
3.9%
I6678
 
3.8%
P5795
 
3.3%
Other values (20)32825
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)174459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A43027
24.7%
C18279
10.5%
O16411
 
9.4%
R15778
 
9.0%
M11309
 
6.5%
D10048
 
5.8%
S7510
 
4.3%
V6799
 
3.9%
I6678
 
3.8%
P5795
 
3.3%
Other values (20)32825
18.8%
Distinct637
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2025-11-08T22:51:06.867685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length54
Median length30
Mean length12.224523
Min length3

Characters and Unicode

Total characters351235
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)0.1%

Sample

1st rowI.E. COL. COLOMBIA
2nd rowI.E. COL. COLOMBIA
3rd rowI.E. COL. COLOMBIA
4th rowI.E. COL. COLOMBIA
5th rowI.E. COL. COLOMBIA
ValueCountFrequency (%)
la1752
 
3.2%
ca1258
 
2.3%
1206
 
2.2%
san1117
 
2.0%
u918
 
1.7%
centro835
 
1.5%
i.e826
 
1.5%
el818
 
1.5%
las796
 
1.4%
estación719
 
1.3%
Other values (593)45029
81.5%
2025-11-08T22:51:07.110892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A48599
13.8%
26542
 
7.6%
E25628
 
7.3%
I22946
 
6.5%
O22514
 
6.4%
N21529
 
6.1%
L21326
 
6.1%
R21033
 
6.0%
C20264
 
5.8%
T15510
 
4.4%
Other values (51)105344
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)351235
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A48599
13.8%
26542
 
7.6%
E25628
 
7.3%
I22946
 
6.5%
O22514
 
6.4%
N21529
 
6.1%
L21326
 
6.1%
R21033
 
6.0%
C20264
 
5.8%
T15510
 
4.4%
Other values (51)105344
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)351235
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A48599
13.8%
26542
 
7.6%
E25628
 
7.3%
I22946
 
6.5%
O22514
 
6.4%
N21529
 
6.1%
L21326
 
6.1%
R21033
 
6.0%
C20264
 
5.8%
T15510
 
4.4%
Other values (51)105344
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)351235
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A48599
13.8%
26542
 
7.6%
E25628
 
7.3%
I22946
 
6.5%
O22514
 
6.4%
N21529
 
6.1%
L21326
 
6.1%
R21033
 
6.0%
C20264
 
5.8%
T15510
 
4.4%
Other values (51)105344
30.0%

Latitud
Real number (ℝ)

High correlation 

Distinct599
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1043421
Minimum1.216489
Maximum12.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size224.6 KiB
2025-11-08T22:51:07.195475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.216489
5-th percentile3.42826
Q14.631767
median6.059036
Q36.4625
95-th percentile11.0149
Maximum12.23
Range11.013511
Interquartile range (IQR)1.830733

Descriptive statistics

Standard deviation2.1472782
Coefficient of variation (CV)0.35176243
Kurtosis0.53461037
Mean6.1043421
Median Absolute Deviation (MAD)1.325762
Skewness1.0745289
Sum175389.96
Variance4.6108038
MonotonicityNot monotonic
2025-11-08T22:51:07.293033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.62505528
 
1.8%
4.658467526
 
1.8%
4.6907481
 
1.7%
4.595617474
 
1.6%
4.631767458
 
1.6%
6.211806419
 
1.5%
4.576283368
 
1.3%
6.330697363
 
1.3%
4.71035336
 
1.2%
4.783733324
 
1.1%
Other values (589)24455
85.1%
ValueCountFrequency (%)
1.2164896
 
< 0.1%
1.22379616
 
0.1%
2.4380554
 
< 0.1%
2.44516134
 
0.1%
2.476861105
0.4%
2.9288917
 
< 0.1%
2.9305877782
 
< 0.1%
2.93067812
 
< 0.1%
2.962522
 
0.1%
2.9625032
 
< 0.1%
ValueCountFrequency (%)
12.2336
 
0.1%
11.54752
 
< 0.1%
11.54688978
0.3%
11.54682
 
< 0.1%
11.51192
 
< 0.1%
11.276619136
0.5%
11.276492118
0.4%
11.25263915
 
0.1%
11.25080412
 
< 0.1%
11.24044612
 
< 0.1%

Longitud
Real number (ℝ)

High correlation 

Distinct596
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-74.658257
Minimum-77.283628
Maximum-70.743889
Zeros0
Zeros (%)0.0%
Negative28732
Negative (%)100.0%
Memory size224.6 KiB
2025-11-08T22:51:07.375729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-77.283628
5-th percentile-76.521225
Q1-75.581111
median-74.222547
Q3-74.030417
95-th percentile-72.906131
Maximum-70.743889
Range6.5397391
Interquartile range (IQR)1.550694

Descriptive statistics

Standard deviation1.1331777
Coefficient of variation (CV)-0.015178196
Kurtosis-0.9908685
Mean-74.658257
Median Absolute Deviation (MAD)1.1460635
Skewness0.022725351
Sum-2145081
Variance1.2840916
MonotonicityNot monotonic
2025-11-08T22:51:07.463382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-74.083967638
 
2.2%
-74.161333528
 
1.8%
-74.082483481
 
1.7%
-74.148583474
 
1.6%
-74.117483458
 
1.6%
-75.581111447
 
1.6%
-74.130967368
 
1.3%
-75.568669363
 
1.3%
-74.030417336
 
1.2%
-74.043783324
 
1.1%
Other values (586)24315
84.6%
ValueCountFrequency (%)
-77.28362816
 
0.1%
-77.2829446
 
< 0.1%
-77.0689726
 
< 0.1%
-76.64611112
 
< 0.1%
-76.613054
 
< 0.1%
-76.60544434
 
0.1%
-76.566734105
0.4%
-76.549613120
0.4%
-76.54934794
0.3%
-76.5442580
0.3%
ValueCountFrequency (%)
-70.74388894
 
< 0.1%
-70.757518
0.1%
-70.75777784
 
< 0.1%
-70.76138894
 
< 0.1%
-72.0336
0.1%
-72.38555622
0.1%
-72.38777784
 
< 0.1%
-72.39055564
 
< 0.1%
-72.40361114
 
< 0.1%
-72.40555564
 
< 0.1%

Variable
Categorical

High correlation 

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
PM10
5138 
PM2.5
3158 
VViento
2508 
DViento
2198 
PLiquida
1854 
Other values (15)
13876 

Length

Max length8
Median length6
Mean length4.8802729
Min length1

Characters and Unicode

Total characters140220
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDViento
2nd rowDViento
3rd rowPLiquida
4th rowP
5th rowP

Common Values

ValueCountFrequency (%)
PM105138
17.9%
PM2.53158
11.0%
VViento2508
8.7%
DViento2198
 
7.7%
PLiquida1854
 
6.5%
SO21758
 
6.1%
TAire21668
 
5.8%
O31481
 
5.2%
HAire21386
 
4.8%
P1346
 
4.7%
Other values (10)6237
21.7%

Length

2025-11-08T22:51:07.552015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pm105138
17.9%
pm2.53158
11.0%
vviento2508
8.7%
dviento2198
 
7.7%
pliquida1854
 
6.5%
so21758
 
6.1%
taire21668
 
5.8%
o31481
 
5.2%
haire21386
 
4.8%
p1346
 
4.7%
Other values (10)6237
21.7%

Most occurring characters

ValueCountFrequency (%)
i13154
 
9.4%
P11978
 
8.5%
e9446
 
6.7%
29110
 
6.5%
M8296
 
5.9%
V7346
 
5.2%
06528
 
4.7%
16528
 
4.7%
O5976
 
4.3%
o5906
 
4.2%
Other values (23)55952
39.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)140220
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i13154
 
9.4%
P11978
 
8.5%
e9446
 
6.7%
29110
 
6.5%
M8296
 
5.9%
V7346
 
5.2%
06528
 
4.7%
16528
 
4.7%
O5976
 
4.3%
o5906
 
4.2%
Other values (23)55952
39.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)140220
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i13154
 
9.4%
P11978
 
8.5%
e9446
 
6.7%
29110
 
6.5%
M8296
 
5.9%
V7346
 
5.2%
06528
 
4.7%
16528
 
4.7%
O5976
 
4.3%
o5906
 
4.2%
Other values (23)55952
39.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)140220
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i13154
 
9.4%
P11978
 
8.5%
e9446
 
6.7%
29110
 
6.5%
M8296
 
5.9%
V7346
 
5.2%
06528
 
4.7%
16528
 
4.7%
O5976
 
4.3%
o5906
 
4.2%
Other values (23)55952
39.9%

Unidades
Categorical

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
ugm3
14754 
ms
2508 
Celsius
2482 
perc
2258 
deg
2198 
Other values (4)
4532 

Length

Max length7
Median length4
Mean length3.8372546
Min length2

Characters and Unicode

Total characters110252
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdeg
2nd rowdeg
3rd rowmm
4th rowmmHg
5th rowmmHg

Common Values

ValueCountFrequency (%)
ugm314754
51.4%
ms2508
 
8.7%
Celsius2482
 
8.6%
perc2258
 
7.9%
deg2198
 
7.7%
mm1854
 
6.5%
mmHg1346
 
4.7%
Wm21200
 
4.2%
MEDh132
 
0.5%

Length

2025-11-08T22:51:07.647858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T22:51:07.726948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ugm314754
51.4%
ms2508
 
8.7%
celsius2482
 
8.6%
perc2258
 
7.9%
deg2198
 
7.7%
mm1854
 
6.5%
mmhg1346
 
4.7%
wm21200
 
4.2%
medh132
 
0.5%

Most occurring characters

ValueCountFrequency (%)
m24862
22.6%
g18298
16.6%
u17236
15.6%
314754
13.4%
s7472
 
6.8%
e6938
 
6.3%
C2482
 
2.3%
l2482
 
2.3%
i2482
 
2.3%
p2258
 
2.0%
Other values (10)10988
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)110252
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m24862
22.6%
g18298
16.6%
u17236
15.6%
314754
13.4%
s7472
 
6.8%
e6938
 
6.3%
C2482
 
2.3%
l2482
 
2.3%
i2482
 
2.3%
p2258
 
2.0%
Other values (10)10988
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)110252
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m24862
22.6%
g18298
16.6%
u17236
15.6%
314754
13.4%
s7472
 
6.8%
e6938
 
6.3%
C2482
 
2.3%
l2482
 
2.3%
i2482
 
2.3%
p2258
 
2.0%
Other values (10)10988
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)110252
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m24862
22.6%
g18298
16.6%
u17236
15.6%
314754
13.4%
s7472
 
6.8%
e6938
 
6.3%
C2482
 
2.3%
l2482
 
2.3%
i2482
 
2.3%
p2258
 
2.0%
Other values (10)10988
10.0%

Tiempo de exposición (horas)
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
24
14894 
1
12036 
8
1592 
3
 
210

Length

Max length2
Median length2
Mean length1.5183767
Min length1

Characters and Unicode

Total characters43626
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row24
3rd row1
4th row1
5th row24

Common Values

ValueCountFrequency (%)
2414894
51.8%
112036
41.9%
81592
 
5.5%
3210
 
0.7%

Length

2025-11-08T22:51:07.810609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T22:51:07.881812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2414894
51.8%
112036
41.9%
81592
 
5.5%
3210
 
0.7%

Most occurring characters

ValueCountFrequency (%)
214894
34.1%
414894
34.1%
112036
27.6%
81592
 
3.6%
3210
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)43626
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
214894
34.1%
414894
34.1%
112036
27.6%
81592
 
3.6%
3210
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)43626
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
214894
34.1%
414894
34.1%
112036
27.6%
81592
 
3.6%
3210
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)43626
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
214894
34.1%
414894
34.1%
112036
27.6%
81592
 
3.6%
3210
 
0.5%

Año
Categorical

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2,022
3882 
2,021
3720 
2,020
3196 
2,017
2705 
2,018
2310 
Other values (8)
12919 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters143660
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2,011
2nd row2,011
3rd row2,011
4th row2,011
5th row2,011

Common Values

ValueCountFrequency (%)
2,0223882
13.5%
2,0213720
12.9%
2,0203196
11.1%
2,0172705
9.4%
2,0182310
8.0%
2,0161932
6.7%
2,0191864
6.5%
2,0131824
6.3%
2,0151806
6.3%
2,0141728
6.0%
Other values (3)3765
13.1%

Length

2025-11-08T22:51:07.959422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,0223882
13.5%
2,0213720
12.9%
2,0203196
11.1%
2,0172705
9.4%
2,0182310
8.0%
2,0161932
6.7%
2,0191864
6.5%
2,0131824
6.3%
2,0151806
6.3%
2,0141728
6.0%
Other values (3)3765
13.1%

Most occurring characters

ValueCountFrequency (%)
245883
31.9%
031928
22.2%
,28732
20.0%
122095
15.4%
72705
 
1.9%
32677
 
1.9%
82310
 
1.6%
61932
 
1.3%
91864
 
1.3%
51806
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)143660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
245883
31.9%
031928
22.2%
,28732
20.0%
122095
15.4%
72705
 
1.9%
32677
 
1.9%
82310
 
1.6%
61932
 
1.3%
91864
 
1.3%
51806
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)143660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
245883
31.9%
031928
22.2%
,28732
20.0%
122095
15.4%
72705
 
1.9%
32677
 
1.9%
82310
 
1.6%
61932
 
1.3%
91864
 
1.3%
51806
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)143660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
245883
31.9%
031928
22.2%
,28732
20.0%
122095
15.4%
72705
 
1.9%
32677
 
1.9%
82310
 
1.6%
61932
 
1.3%
91864
 
1.3%
51806
 
1.3%
Distinct2823
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-11-08T22:51:08.136410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length4
Mean length3.7667061
Min length1

Characters and Unicode

Total characters108225
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)0.2%

Sample

1st row256.8
2nd row257.4
3rd row4
4th row645.9
5th row645.9
ValueCountFrequency (%)
0.1475
 
1.7%
1364
 
1.3%
1.4218
 
0.8%
0.2209
 
0.7%
1.2184
 
0.6%
1.1180
 
0.6%
0.9177
 
0.6%
0.8174
 
0.6%
0.6154
 
0.5%
1.3150
 
0.5%
Other values (2813)26447
92.0%
2025-11-08T22:51:08.412353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
.25500
23.6%
113929
12.9%
212205
11.3%
39465
 
8.7%
47800
 
7.2%
67633
 
7.1%
57328
 
6.8%
77165
 
6.6%
86599
 
6.1%
95556
 
5.1%
Other values (2)5045
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)108225
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.25500
23.6%
113929
12.9%
212205
11.3%
39465
 
8.7%
47800
 
7.2%
67633
 
7.1%
57328
 
6.8%
77165
 
6.6%
86599
 
6.1%
95556
 
5.1%
Other values (2)5045
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)108225
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.25500
23.6%
113929
12.9%
212205
11.3%
39465
 
8.7%
47800
 
7.2%
67633
 
7.1%
57328
 
6.8%
77165
 
6.6%
86599
 
6.1%
95556
 
5.1%
Other values (2)5045
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)108225
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.25500
23.6%
113929
12.9%
212205
11.3%
39465
 
8.7%
47800
 
7.2%
67633
 
7.1%
57328
 
6.8%
77165
 
6.6%
86599
 
6.1%
95556
 
5.1%
Other values (2)5045
 
4.7%

Suma
Text

Distinct14463
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2025-11-08T22:51:08.570314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length11
Mean length8.3828136
Min length1

Characters and Unicode

Total characters240855
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique849 ?
Unique (%)3.0%

Sample

1st row321,637.5091
2nd row14,984.01
3rd row293.5
4th row2,917,013.928
5th row121,434.69
ValueCountFrequency (%)
47108
 
0.4%
254
 
0.2%
105.028
 
< 0.1%
120.48
 
< 0.1%
968.86
 
< 0.1%
852.46
 
< 0.1%
2.86
 
< 0.1%
1,072.86
 
< 0.1%
22,985.526
 
< 0.1%
12.996
 
< 0.1%
Other values (14453)28518
99.3%
2025-11-08T22:51:08.810467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
,26808
11.1%
.25847
10.7%
125142
10.4%
221770
9.0%
319469
8.1%
519076
7.9%
418710
7.8%
618211
7.6%
717892
7.4%
817267
7.2%
Other values (2)30663
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)240855
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
,26808
11.1%
.25847
10.7%
125142
10.4%
221770
9.0%
319469
8.1%
519076
7.9%
418710
7.8%
618211
7.6%
717892
7.4%
817267
7.2%
Other values (2)30663
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)240855
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
,26808
11.1%
.25847
10.7%
125142
10.4%
221770
9.0%
319469
8.1%
519076
7.9%
418710
7.8%
618211
7.6%
717892
7.4%
817267
7.2%
Other values (2)30663
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)240855
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
,26808
11.1%
.25847
10.7%
125142
10.4%
221770
9.0%
319469
8.1%
519076
7.9%
418710
7.8%
618211
7.6%
717892
7.4%
817267
7.2%
Other values (2)30663
12.7%
Distinct3821
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-11-08T22:51:08.999691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.7057288
Min length1

Characters and Unicode

Total characters106473
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)0.6%

Sample

1st row1,411
2nd row59
3rd row74
4th row4,516
5th row188
ValueCountFrequency (%)
365375
 
1.3%
31274
 
1.0%
364266
 
0.9%
363244
 
0.8%
361187
 
0.7%
20167
 
0.6%
355160
 
0.6%
352155
 
0.5%
359154
 
0.5%
360143
 
0.5%
Other values (3811)26607
92.6%
2025-11-08T22:51:09.272814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
313650
12.8%
,12720
11.9%
111287
10.6%
810494
9.9%
210039
9.4%
79655
9.1%
59170
8.6%
69095
8.5%
48427
7.9%
06197
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)106473
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
313650
12.8%
,12720
11.9%
111287
10.6%
810494
9.9%
210039
9.4%
79655
9.1%
59170
8.6%
69095
8.5%
48427
7.9%
06197
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)106473
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
313650
12.8%
,12720
11.9%
111287
10.6%
810494
9.9%
210039
9.4%
79655
9.1%
59170
8.6%
69095
8.5%
48427
7.9%
06197
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)106473
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
313650
12.8%
,12720
11.9%
111287
10.6%
810494
9.9%
210039
9.4%
79655
9.1%
59170
8.6%
69095
8.5%
48427
7.9%
06197
5.8%

Representatividad Temporal
Real number (ℝ)

Distinct107
Distinct (%)0.4%
Missing148
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean66.134131
Minimum0
Maximum167
Zeros124
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size224.6 KiB
2025-11-08T22:51:09.371108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q138
median81
Q394
95-th percentile99
Maximum167
Range167
Interquartile range (IQR)56

Descriptive statistics

Standard deviation33.027271
Coefficient of variation (CV)0.49939827
Kurtosis-0.98633987
Mean66.134131
Median Absolute Deviation (MAD)17
Skewness-0.71825173
Sum1890378
Variance1090.8006
MonotonicityNot monotonic
2025-11-08T22:51:09.458794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
991547
 
5.4%
1001368
 
4.8%
981171
 
4.1%
971073
 
3.7%
96955
 
3.3%
95864
 
3.0%
92822
 
2.9%
93778
 
2.7%
8735
 
2.6%
94724
 
2.5%
Other values (97)18547
64.6%
ValueCountFrequency (%)
0124
 
0.4%
1301
1.0%
2267
 
0.9%
3138
 
0.5%
4337
1.2%
5338
1.2%
6246
 
0.9%
7299
1.0%
8735
2.6%
998
 
0.3%
ValueCountFrequency (%)
1672
 
< 0.1%
1152
 
< 0.1%
1122
 
< 0.1%
1036
 
< 0.1%
1026
 
< 0.1%
1016
 
< 0.1%
1001368
4.8%
991547
5.4%
981171
4.1%
971073
3.7%

Excedencias limite actual
Real number (ℝ)

High correlation  Zeros 

Distinct608
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.825491
Minimum0
Maximum3292
Zeros23462
Zeros (%)81.7%
Negative0
Negative (%)0.0%
Memory size224.6 KiB
2025-11-08T22:51:09.543289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile102
Maximum3292
Range3292
Interquartile range (IQR)0

Descriptive statistics

Standard deviation146.75311
Coefficient of variation (CV)5.4706587
Kurtosis114.99405
Mean26.825491
Median Absolute Deviation (MAD)0
Skewness9.2496726
Sum770750
Variance21536.474
MonotonicityNot monotonic
2025-11-08T22:51:09.632706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
023462
81.7%
1752
 
2.6%
2415
 
1.4%
3259
 
0.9%
4206
 
0.7%
5160
 
0.6%
6127
 
0.4%
7124
 
0.4%
8116
 
0.4%
995
 
0.3%
Other values (598)3016
 
10.5%
ValueCountFrequency (%)
023462
81.7%
1752
 
2.6%
2415
 
1.4%
3259
 
0.9%
4206
 
0.7%
5160
 
0.6%
6127
 
0.4%
7124
 
0.4%
8116
 
0.4%
995
 
0.3%
ValueCountFrequency (%)
32922
< 0.1%
29182
< 0.1%
28572
< 0.1%
26962
< 0.1%
26702
< 0.1%
25712
< 0.1%
25452
< 0.1%
25182
< 0.1%
20142
< 0.1%
19772
< 0.1%

Porcentaje excedencias limite actual
Real number (ℝ)

High correlation  Zeros 

Distinct1059
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.94618231
Minimum0
Maximum80.77
Zeros23464
Zeros (%)81.7%
Negative0
Negative (%)0.0%
Memory size224.6 KiB
2025-11-08T22:51:09.729292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.5545
Maximum80.77
Range80.77
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.1465051
Coefficient of variation (CV)4.3823533
Kurtosis95.540521
Mean0.94618231
Median Absolute Deviation (MAD)0
Skewness8.4110732
Sum27185.71
Variance17.193505
MonotonicityNot monotonic
2025-11-08T22:51:09.821154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
023464
81.7%
0.2870
 
0.2%
0.0157
 
0.2%
0.0554
 
0.2%
0.0453
 
0.2%
0.2953
 
0.2%
0.0345
 
0.2%
0.340
 
0.1%
0.3139
 
0.1%
0.0238
 
0.1%
Other values (1049)4819
 
16.8%
ValueCountFrequency (%)
023464
81.7%
0.0157
 
0.2%
0.0238
 
0.1%
0.0345
 
0.2%
0.0453
 
0.2%
0.0554
 
0.2%
0.0620
 
0.1%
0.0731
 
0.1%
0.0828
 
0.1%
0.0919
 
0.1%
ValueCountFrequency (%)
80.772
< 0.1%
71.762
< 0.1%
70.422
< 0.1%
702
< 0.1%
69.172
< 0.1%
66.672
< 0.1%
66.22
< 0.1%
62.072
< 0.1%
604
< 0.1%
58.332
< 0.1%

Mediana
Text

Distinct2652
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-11-08T22:51:10.000716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.488619
Min length1

Characters and Unicode

Total characters100235
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)0.2%

Sample

1st row244.2
2nd row257.9
3rd row2.8
4th row646.1
5th row645.9
ValueCountFrequency (%)
01405
 
4.9%
1395
 
1.4%
0.9225
 
0.8%
0.8211
 
0.7%
1.2194
 
0.7%
1.1184
 
0.6%
0.4169
 
0.6%
0.6164
 
0.6%
0.5161
 
0.6%
1.4156
 
0.5%
Other values (2642)25468
88.6%
2025-11-08T22:51:10.268585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
.22253
22.2%
113506
13.5%
211662
11.6%
38042
 
8.0%
67050
 
7.0%
47026
 
7.0%
56618
 
6.6%
76559
 
6.5%
86176
 
6.2%
05792
 
5.8%
Other values (2)5551
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)100235
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.22253
22.2%
113506
13.5%
211662
11.6%
38042
 
8.0%
67050
 
7.0%
47026
 
7.0%
56618
 
6.6%
76559
 
6.5%
86176
 
6.2%
05792
 
5.8%
Other values (2)5551
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100235
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.22253
22.2%
113506
13.5%
211662
11.6%
38042
 
8.0%
67050
 
7.0%
47026
 
7.0%
56618
 
6.6%
76559
 
6.5%
86176
 
6.2%
05792
 
5.8%
Other values (2)5551
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100235
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.22253
22.2%
113506
13.5%
211662
11.6%
38042
 
8.0%
67050
 
7.0%
47026
 
7.0%
56618
 
6.6%
76559
 
6.5%
86176
 
6.2%
05792
 
5.8%
Other values (2)5551
 
5.5%
Distinct3592
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-11-08T22:51:10.457134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.8776973
Min length1

Characters and Unicode

Total characters111414
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)0.3%

Sample

1st row350.8
2nd row304.2
3rd row12.8
4th row648.1
5th row647.2
ValueCountFrequency (%)
1256
 
0.9%
1.3116
 
0.4%
2.2108
 
0.4%
1.7108
 
0.4%
1.6106
 
0.4%
1.2104
 
0.4%
1.4104
 
0.4%
1.9103
 
0.4%
2102
 
0.4%
1.5102
 
0.4%
Other values (3582)27523
95.8%
2025-11-08T22:51:10.877734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
.23892
21.4%
111566
10.4%
210981
9.9%
310695
9.6%
58830
 
7.9%
68463
 
7.6%
48428
 
7.6%
88074
 
7.2%
77849
 
7.0%
97295
 
6.5%
Other values (2)5341
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)111414
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.23892
21.4%
111566
10.4%
210981
9.9%
310695
9.6%
58830
 
7.9%
68463
 
7.6%
48428
 
7.6%
88074
 
7.2%
77849
 
7.0%
97295
 
6.5%
Other values (2)5341
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)111414
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.23892
21.4%
111566
10.4%
210981
9.9%
310695
9.6%
58830
 
7.9%
68463
 
7.6%
48428
 
7.6%
88074
 
7.2%
77849
 
7.0%
97295
 
6.5%
Other values (2)5341
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)111414
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.23892
21.4%
111566
10.4%
210981
9.9%
310695
9.6%
58830
 
7.9%
68463
 
7.6%
48428
 
7.6%
88074
 
7.2%
77849
 
7.0%
97295
 
6.5%
Other values (2)5341
 
4.8%

Máximo
Text

Distinct4214
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-11-08T22:51:11.062684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.0183071
Min length1

Characters and Unicode

Total characters115454
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique105 ?
Unique (%)0.4%

Sample

1st row359.5
2nd row336.8
3rd row23
4th row649.8
5th row647.9
ValueCountFrequency (%)
360572
 
2.0%
100209
 
0.7%
359.9164
 
0.6%
1162
 
0.6%
359.894
 
0.3%
35984
 
0.3%
1.876
 
0.3%
274
 
0.3%
3.169
 
0.2%
337.562
 
0.2%
Other values (4204)27166
94.5%
2025-11-08T22:51:11.334343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
.23052
20.0%
113026
11.3%
311181
9.7%
210710
9.3%
58904
 
7.7%
68776
 
7.6%
98767
 
7.6%
48346
 
7.2%
88163
 
7.1%
77743
 
6.7%
Other values (2)6786
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)115454
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.23052
20.0%
113026
11.3%
311181
9.7%
210710
9.3%
58904
 
7.7%
68776
 
7.6%
98767
 
7.6%
48346
 
7.2%
88163
 
7.1%
77743
 
6.7%
Other values (2)6786
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)115454
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.23052
20.0%
113026
11.3%
311181
9.7%
210710
9.3%
58904
 
7.7%
68776
 
7.6%
98767
 
7.6%
48346
 
7.2%
88163
 
7.1%
77743
 
6.7%
Other values (2)6786
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)115454
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.23052
20.0%
113026
11.3%
311181
9.7%
210710
9.3%
58904
 
7.7%
68776
 
7.6%
98767
 
7.6%
48346
 
7.2%
88163
 
7.1%
77743
 
6.7%
Other values (2)6786
 
5.9%
Distinct9695
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2025-11-08T22:51:11.480396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2273
Median length2227
Mean length30.610052
Min length5

Characters and Unicode

Total characters879488
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique472 ?
Unique (%)1.6%

Sample

1st row29/11/2011 1:00
2nd row16/11/2011 0:00
3rd row20/12/2011 2:00
4th row12/09/2011 10:00
5th row20/10/2011 0:00
ValueCountFrequency (%)
22152
 
19.5%
0:005830
 
5.1%
12:003344
 
2.9%
02/07/20162216
 
1.9%
01/07/20162126
 
1.9%
10:00:001614
 
1.4%
12:00:001514
 
1.3%
11:00:001465
 
1.3%
05:00:001448
 
1.3%
07:00:001446
 
1.3%
Other values (5891)70637
62.1%
2025-11-08T22:51:11.736833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0254903
29.0%
2108102
12.3%
1103097
11.7%
/94274
 
10.7%
92986
 
10.6%
:62969
 
7.2%
625002
 
2.8%
323365
 
2.7%
-22254
 
2.5%
421440
 
2.4%
Other values (12)71096
 
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)879488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0254903
29.0%
2108102
12.3%
1103097
11.7%
/94274
 
10.7%
92986
 
10.6%
:62969
 
7.2%
625002
 
2.8%
323365
 
2.7%
-22254
 
2.5%
421440
 
2.4%
Other values (12)71096
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)879488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0254903
29.0%
2108102
12.3%
1103097
11.7%
/94274
 
10.7%
92986
 
10.6%
:62969
 
7.2%
625002
 
2.8%
323365
 
2.7%
-22254
 
2.5%
421440
 
2.4%
Other values (12)71096
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)879488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0254903
29.0%
2108102
12.3%
1103097
11.7%
/94274
 
10.7%
92986
 
10.6%
:62969
 
7.2%
625002
 
2.8%
323365
 
2.7%
-22254
 
2.5%
421440
 
2.4%
Other values (12)71096
 
8.1%

Mínimo
Text

Distinct1532
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-11-08T22:51:11.910676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length7
Mean length2.7678895
Min length1

Characters and Unicode

Total characters79527
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st row1.3
2nd row99.7
3rd row1.5
4th row641.6
5th row644
ValueCountFrequency (%)
06024
 
21.0%
11175
 
4.1%
0.1929
 
3.2%
0.2808
 
2.8%
0.3667
 
2.3%
0.4475
 
1.7%
0.5401
 
1.4%
0.6382
 
1.3%
0.7281
 
1.0%
0.8266
 
0.9%
Other values (1508)17324
60.3%
2025-11-08T22:51:12.166644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
.18987
23.9%
011881
14.9%
111454
14.4%
26863
 
8.6%
35017
 
6.3%
44927
 
6.2%
54920
 
6.2%
64652
 
5.8%
73772
 
4.7%
83704
 
4.7%
Other values (3)3350
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)79527
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.18987
23.9%
011881
14.9%
111454
14.4%
26863
 
8.6%
35017
 
6.3%
44927
 
6.2%
54920
 
6.2%
64652
 
5.8%
73772
 
4.7%
83704
 
4.7%
Other values (3)3350
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)79527
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.18987
23.9%
011881
14.9%
111454
14.4%
26863
 
8.6%
35017
 
6.3%
44927
 
6.2%
54920
 
6.2%
64652
 
5.8%
73772
 
4.7%
83704
 
4.7%
Other values (3)3350
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)79527
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.18987
23.9%
011881
14.9%
111454
14.4%
26863
 
8.6%
35017
 
6.3%
44927
 
6.2%
54920
 
6.2%
64652
 
5.8%
73772
 
4.7%
83704
 
4.7%
Other values (3)3350
 
4.2%
Distinct10200
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2025-11-08T22:51:12.311142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2296
Median length2227
Mean length91.339099
Min length5

Characters and Unicode

Total characters2624355
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique571 ?
Unique (%)2.0%

Sample

1st row29/11/2011 7:00
2nd row8/11/2011 0:00
3rd row07/11/2011 23:00:00 - 08/11/2011 05:00:00 - 13/11/2011 21:00:00 - 14/11/2011 20:00:00 - 18/11/2011 22:00:00 - 19/11/2011 03:00:00 - 21/11/2011 19:00:00 - 23/11/2011 19:00:00 - 23/11/2011 20:00:00 - 23/11/2011 21:00:00 - 24/11/2011 19:00:00 - 28/11/2011 20:00:00 - 30/11/2011 15:00:00 - 05/12/2011 03:00:00 - 05/12/2011 04:00:00 - 08/12/2011 03:00:00 - 13/12/2011 09:00:00 - 21/12/2011 05:00:00 - 23/12/2011 02:00:00
4th row27/10/2011 17:00
5th row27/10/2011 0:00
ValueCountFrequency (%)
100630
29.2%
12:00:0015851
 
4.6%
00:00:009363
 
2.7%
04:00:007706
 
2.2%
03:00:007147
 
2.1%
05:00:006786
 
2.0%
02:00:006216
 
1.8%
06:00:006086
 
1.8%
01:00:005440
 
1.6%
07:00:005097
 
1.5%
Other values (6503)174621
50.6%
2025-11-08T22:51:12.548097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0797600
30.4%
361409
13.8%
2312496
 
11.9%
1262650
 
10.0%
/241998
 
9.2%
:216784
 
8.3%
-106540
 
4.1%
349432
 
1.9%
747191
 
1.8%
644566
 
1.7%
Other values (12)183689
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2624355
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0797600
30.4%
361409
13.8%
2312496
 
11.9%
1262650
 
10.0%
/241998
 
9.2%
:216784
 
8.3%
-106540
 
4.1%
349432
 
1.9%
747191
 
1.8%
644566
 
1.7%
Other values (12)183689
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2624355
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0797600
30.4%
361409
13.8%
2312496
 
11.9%
1262650
 
10.0%
/241998
 
9.2%
:216784
 
8.3%
-106540
 
4.1%
349432
 
1.9%
747191
 
1.8%
644566
 
1.7%
Other values (12)183689
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2624355
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0797600
30.4%
361409
13.8%
2312496
 
11.9%
1262650
 
10.0%
/241998
 
9.2%
:216784
 
8.3%
-106540
 
4.1%
349432
 
1.9%
747191
 
1.8%
644566
 
1.7%
Other values (12)183689
 
7.0%

Días de excedencias
Real number (ℝ)

High correlation  Zeros 

Distinct256
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1496937
Minimum0
Maximum327
Zeros23464
Zeros (%)81.7%
Negative0
Negative (%)0.0%
Memory size224.6 KiB
2025-11-08T22:51:12.650529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile44
Maximum327
Range327
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.204741
Coefficient of variation (CV)4.0847541
Kurtosis37.58684
Mean7.1496937
Median Absolute Deviation (MAD)0
Skewness5.7316839
Sum205425
Variance852.91689
MonotonicityNot monotonic
2025-11-08T22:51:12.745101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
023464
81.7%
1856
 
3.0%
2450
 
1.6%
3262
 
0.9%
4247
 
0.9%
5176
 
0.6%
6164
 
0.6%
7145
 
0.5%
8120
 
0.4%
9110
 
0.4%
Other values (246)2738
 
9.5%
ValueCountFrequency (%)
023464
81.7%
1856
 
3.0%
2450
 
1.6%
3262
 
0.9%
4247
 
0.9%
5176
 
0.6%
6164
 
0.6%
7145
 
0.5%
8120
 
0.4%
9110
 
0.4%
ValueCountFrequency (%)
3272
 
< 0.1%
3232
 
< 0.1%
3102
 
< 0.1%
3062
 
< 0.1%
2991
 
< 0.1%
2942
 
< 0.1%
2915
< 0.1%
2904
< 0.1%
2882
 
< 0.1%
2872
 
< 0.1%

Código del Departamento
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.078275
Minimum5
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size224.6 KiB
2025-11-08T22:51:12.825700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q15
median11
Q344
95-th percentile76
Maximum85
Range80
Interquartile range (IQR)39

Descriptive statistics

Standard deviation25.279328
Coefficient of variation (CV)1.008017
Kurtosis-0.4021864
Mean25.078275
Median Absolute Deviation (MAD)6
Skewness1.1014324
Sum720549
Variance639.04441
MonotonicityNot monotonic
2025-11-08T22:51:12.905668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
58633
30.0%
116141
21.4%
762936
 
10.2%
681910
 
6.6%
251700
 
5.9%
151653
 
5.8%
201072
 
3.7%
471016
 
3.5%
8839
 
2.9%
44780
 
2.7%
Other values (14)2052
 
7.1%
ValueCountFrequency (%)
58633
30.0%
8839
 
2.9%
116141
21.4%
13270
 
0.9%
151653
 
5.8%
17208
 
0.7%
19295
 
1.0%
201072
 
3.7%
23188
 
0.7%
251700
 
5.9%
ValueCountFrequency (%)
8522
 
0.1%
8150
 
0.2%
762936
10.2%
73105
 
0.4%
681910
6.6%
66172
 
0.6%
6338
 
0.1%
54123
 
0.4%
5222
 
0.1%
50495
 
1.7%

Nombre del Departamento
Categorical

High correlation 

Distinct48
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
ANTIOQUIA
8486 
BOGOTÁ, D.C.
4285 
VALLE DEL CAUCA
2868 
SANTANDER
1880 
BOGOTA, D.C.
1680 
Other values (43)
9533 

Length

Max length18
Median length15
Mean length9.9640471
Min length4

Characters and Unicode

Total characters286287
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowANTIOQUIA
2nd rowANTIOQUIA
3rd rowANTIOQUIA
4th rowANTIOQUIA
5th rowANTIOQUIA

Common Values

ValueCountFrequency (%)
ANTIOQUIA8486
29.5%
BOGOTÁ, D.C.4285
14.9%
VALLE DEL CAUCA2868
 
10.0%
SANTANDER1880
 
6.5%
BOGOTA, D.C.1680
 
5.8%
CUNDINAMARCA1514
 
5.3%
CESAR1048
 
3.6%
BOYACÁ1030
 
3.6%
MAGDALENA986
 
3.4%
LA GUAJIRA764
 
2.7%
Other values (38)4191
14.6%

Length

2025-11-08T22:51:13.008812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
antioquia8633
20.7%
d.c6141
14.7%
bogotá4461
10.7%
cauca3231
 
7.7%
valle2936
 
7.0%
del2936
 
7.0%
santander2033
 
4.9%
cundinamarca1700
 
4.1%
bogota1680
 
4.0%
boyacá1089
 
2.6%
Other values (23)6931
16.6%

Most occurring characters

ValueCountFrequency (%)
A48132
16.8%
O23594
 
8.2%
I20456
 
7.1%
C19724
 
6.9%
T18808
 
6.6%
N17527
 
6.1%
D14195
 
5.0%
U14060
 
4.9%
13039
 
4.6%
.12282
 
4.3%
Other values (39)84470
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)286287
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A48132
16.8%
O23594
 
8.2%
I20456
 
7.1%
C19724
 
6.9%
T18808
 
6.6%
N17527
 
6.1%
D14195
 
5.0%
U14060
 
4.9%
13039
 
4.6%
.12282
 
4.3%
Other values (39)84470
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)286287
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A48132
16.8%
O23594
 
8.2%
I20456
 
7.1%
C19724
 
6.9%
T18808
 
6.6%
N17527
 
6.1%
D14195
 
5.0%
U14060
 
4.9%
13039
 
4.6%
.12282
 
4.3%
Other values (39)84470
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)286287
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A48132
16.8%
O23594
 
8.2%
I20456
 
7.1%
C19724
 
6.9%
T18808
 
6.6%
N17527
 
6.1%
D14195
 
5.0%
U14060
 
4.9%
13039
 
4.6%
.12282
 
4.3%
Other values (39)84470
29.5%

Código del Municipio
Real number (ℝ)

High correlation 

Distinct186
Distinct (%)0.6%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean25276.643
Minimum5001
Maximum85001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size224.6 KiB
2025-11-08T22:51:13.100777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5001
5-th percentile5001
Q15440
median11001
Q344279
95-th percentile76001
Maximum85001
Range80000
Interquartile range (IQR)38839

Descriptive statistics

Standard deviation25267.373
Coefficient of variation (CV)0.99963327
Kurtosis-0.40083048
Mean25276.643
Median Absolute Deviation (MAD)5922
Skewness1.1001653
Sum7.2614739 × 108
Variance6.3844014 × 108
MonotonicityNot monotonic
2025-11-08T22:51:13.218152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110016145
21.4%
50012773
 
9.7%
760012064
 
7.2%
680011326
 
4.6%
5360945
 
3.3%
15759918
 
3.2%
5308844
 
2.9%
47001706
 
2.5%
8001609
 
2.1%
15491515
 
1.8%
Other values (176)11883
41.4%
ValueCountFrequency (%)
50012773
9.7%
5030124
 
0.4%
50316
 
< 0.1%
503418
 
0.1%
50366
 
< 0.1%
503810
 
< 0.1%
50402
 
< 0.1%
504256
 
0.2%
504410
 
< 0.1%
50594
 
< 0.1%
ValueCountFrequency (%)
8500142
 
0.1%
8100130
 
0.1%
768954
 
< 0.1%
76892338
1.2%
7683453
 
0.2%
76520283
1.0%
7636480
 
0.3%
761478
 
< 0.1%
7613086
 
0.3%
7611114
 
< 0.1%
Distinct301
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-11-08T22:51:13.413119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length21
Mean length9.0287484
Min length4

Characters and Unicode

Total characters259414
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowGIRARDOTA
2nd rowGIRARDOTA
3rd rowGIRARDOTA
4th rowGIRARDOTA
5th rowGIRARDOTA
ValueCountFrequency (%)
d.c6145
 
15.8%
bogotá4285
 
11.0%
cali2064
 
5.3%
medellín1860
 
4.8%
bogota1860
 
4.8%
bucaramanga1326
 
3.4%
sogamoso918
 
2.4%
medellin913
 
2.3%
girardota844
 
2.2%
santa807
 
2.1%
Other values (232)17965
46.1%
2025-11-08T22:51:13.684697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A35645
 
13.7%
O22958
 
8.8%
C15391
 
5.9%
L14678
 
5.7%
I13319
 
5.1%
D13154
 
5.1%
B12398
 
4.8%
.12290
 
4.7%
G12255
 
4.7%
N12046
 
4.6%
Other values (45)95280
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)259414
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A35645
 
13.7%
O22958
 
8.8%
C15391
 
5.9%
L14678
 
5.7%
I13319
 
5.1%
D13154
 
5.1%
B12398
 
4.8%
.12290
 
4.7%
G12255
 
4.7%
N12046
 
4.6%
Other values (45)95280
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)259414
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A35645
 
13.7%
O22958
 
8.8%
C15391
 
5.9%
L14678
 
5.7%
I13319
 
5.1%
D13154
 
5.1%
B12398
 
4.8%
.12290
 
4.7%
G12255
 
4.7%
N12046
 
4.6%
Other values (45)95280
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)259414
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A35645
 
13.7%
O22958
 
8.8%
C15391
 
5.9%
L14678
 
5.7%
I13319
 
5.1%
D13154
 
5.1%
B12398
 
4.8%
.12290
 
4.7%
G12255
 
4.7%
N12046
 
4.6%
Other values (45)95280
36.7%

Tipo de Estación
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size1.5 MiB
Fija
25773 
Indicativa
2957 

Length

Max length10
Median length4
Mean length4.6175426
Min length4

Characters and Unicode

Total characters132662
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFija
2nd rowFija
3rd rowFija
4th rowFija
5th rowFija

Common Values

ValueCountFrequency (%)
Fija25773
89.7%
Indicativa2957
 
10.3%
(Missing)2
 
< 0.1%

Length

2025-11-08T22:51:13.795263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T22:51:13.875286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fija25773
89.7%
indicativa2957
 
10.3%

Most occurring characters

ValueCountFrequency (%)
i31687
23.9%
a31687
23.9%
F25773
19.4%
j25773
19.4%
I2957
 
2.2%
n2957
 
2.2%
d2957
 
2.2%
c2957
 
2.2%
t2957
 
2.2%
v2957
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)132662
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i31687
23.9%
a31687
23.9%
F25773
19.4%
j25773
19.4%
I2957
 
2.2%
n2957
 
2.2%
d2957
 
2.2%
c2957
 
2.2%
t2957
 
2.2%
v2957
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)132662
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i31687
23.9%
a31687
23.9%
F25773
19.4%
j25773
19.4%
I2957
 
2.2%
n2957
 
2.2%
d2957
 
2.2%
c2957
 
2.2%
t2957
 
2.2%
v2957
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)132662
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i31687
23.9%
a31687
23.9%
F25773
19.4%
j25773
19.4%
I2957
 
2.2%
n2957
 
2.2%
d2957
 
2.2%
c2957
 
2.2%
t2957
 
2.2%
v2957
 
2.2%
Distinct606
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2025-11-08T22:51:14.023437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length27
Mean length26.917026
Min length20

Characters and Unicode

Total characters773380
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowPOINT (-75.443986 6.378517)
2nd rowPOINT (-75.443986 6.378517)
3rd rowPOINT (-75.443986 6.378517)
4th rowPOINT (-75.443986 6.378517)
5th rowPOINT (-75.443986 6.378517)
ValueCountFrequency (%)
point28732
33.3%
74.083967638
 
0.7%
4.62505528
 
0.6%
74.161333528
 
0.6%
4.658467526
 
0.6%
74.082483481
 
0.6%
4.6907481
 
0.6%
74.148583474
 
0.5%
4.595617474
 
0.5%
74.117483458
 
0.5%
Other values (1187)52876
61.3%
2025-11-08T22:51:14.279793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
764834
 
8.4%
57464
 
7.4%
.57464
 
7.4%
651615
 
6.7%
448398
 
6.3%
546683
 
6.0%
146274
 
6.0%
345251
 
5.9%
834826
 
4.5%
232614
 
4.2%
Other values (10)287957
37.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)773380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
764834
 
8.4%
57464
 
7.4%
.57464
 
7.4%
651615
 
6.7%
448398
 
6.3%
546683
 
6.0%
146274
 
6.0%
345251
 
5.9%
834826
 
4.5%
232614
 
4.2%
Other values (10)287957
37.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)773380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
764834
 
8.4%
57464
 
7.4%
.57464
 
7.4%
651615
 
6.7%
448398
 
6.3%
546683
 
6.0%
146274
 
6.0%
345251
 
5.9%
834826
 
4.5%
232614
 
4.2%
Other values (10)287957
37.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)773380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
764834
 
8.4%
57464
 
7.4%
.57464
 
7.4%
651615
 
6.7%
448398
 
6.3%
546683
 
6.0%
146274
 
6.0%
345251
 
5.9%
834826
 
4.5%
232614
 
4.2%
Other values (10)287957
37.2%

Interactions

2025-11-08T22:51:05.081812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:00.435173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:01.051873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:01.674881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:02.315851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:02.954115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:03.747471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:04.419809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:05.159457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:00.511147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:01.128350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:01.760110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:02.391295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:03.033900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:03.825815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:04.500092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:05.238248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:00.588016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:01.203105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:01.838736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:02.469068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:03.128810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:03.908144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:04.591372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:05.319429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:00.663907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:01.289012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:01.917416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:02.548921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:03.322648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:03.993453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:04.670784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:05.401802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:00.737810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:01.365151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:01.995693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:02.636643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:03.404728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:04.076677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:04.750530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:05.485176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:00.821657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:01.442923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:02.073332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:02.715916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:03.490679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:04.170023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:04.831973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:05.577335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:00.896169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:01.519276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:02.149299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:02.793494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:03.571738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:04.252572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:04.911337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:05.663332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:00.973888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:01.596947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:02.237435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:02.873384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:03.665015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:04.334905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T22:51:04.990764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-08T22:51:14.367231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Autoridad AmbientalAñoCódigo del DepartamentoCódigo del MunicipioDías de excedenciasExcedencias limite actualLatitudLongitudNombre del DepartamentoPorcentaje excedencias limite actualRepresentatividad TemporalTiempo de exposición (horas)Tipo de EstaciónUnidadesVariable
Autoridad Ambiental1.0000.1780.9900.9900.0590.0340.8790.8120.8740.0880.2550.1940.7840.1710.174
Año0.1781.0000.1130.1130.0620.0510.0940.0920.3570.0510.0760.0760.1290.0740.129
Código del Departamento0.9900.1131.0000.9860.0390.039-0.2560.2370.9990.045-0.0290.0970.2410.1160.170
Código del Municipio0.9900.1130.9861.0000.0310.031-0.2560.2400.9980.038-0.0550.0960.2410.1160.170
Días de excedencias0.0590.0620.0390.0311.0000.999-0.0300.0340.0600.9950.0560.1290.0670.0850.116
Excedencias limite actual0.0340.0510.0390.0310.9991.000-0.0300.0340.0210.9940.0550.0990.0490.0530.077
Latitud0.8790.094-0.256-0.256-0.030-0.0301.0000.2760.874-0.019-0.1010.1310.3170.1020.156
Longitud0.8120.0920.2370.2400.0340.0340.2761.0000.8190.0380.1070.1000.3020.0880.139
Nombre del Departamento0.8740.3570.9990.9980.0600.0210.8740.8191.0000.0940.1980.1840.5060.1600.164
Porcentaje excedencias limite actual0.0880.0510.0450.0380.9950.994-0.0190.0380.0941.0000.0450.0160.0360.0630.089
Representatividad Temporal0.2550.076-0.029-0.0550.0560.055-0.1010.1070.1980.0451.0000.0670.4840.0690.095
Tiempo de exposición (horas)0.1940.0760.0970.0960.1290.0990.1310.1000.1840.0160.0671.0000.1350.1450.516
Tipo de Estación0.7840.1290.2410.2410.0670.0490.3170.3020.5060.0360.4840.1351.0000.1480.204
Unidades0.1710.0740.1160.1160.0850.0530.1020.0880.1600.0630.0690.1450.1481.0001.000
Variable0.1740.1290.1700.1700.1160.0770.1560.1390.1640.0890.0950.5160.2041.0001.000

Missing values

2025-11-08T22:51:05.785907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-08T22:51:05.927345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-08T22:51:06.071068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ID EstacionAutoridad AmbientalEstaciónLatitudLongitudVariableUnidadesTiempo de exposición (horas)AñoPromedioSumaNo. de datosRepresentatividad TemporalExcedencias limite actualPorcentaje excedencias limite actualMedianaPercentil 98MáximoFechas/horas del máximoMínimoFechas/horas del mínimoDías de excedenciasCódigo del DepartamentoNombre del DepartamentoCódigo del MunicipioNombre del MunicipioTipo de EstaciónUbicacion
09,020AMVAI.E. COL. COLOMBIA6.378517-75.443986DVientodeg12,011256.8321,637.50911,41116.000.0244.2350.8359.529/11/2011 1:001.329/11/2011 7:0005ANTIOQUIA5308.0GIRARDOTAFijaPOINT (-75.443986 6.378517)
19,020AMVAI.E. COL. COLOMBIA6.378517-75.443986DVientodeg242,011257.414,984.015916.000.0257.9304.2336.816/11/2011 0:0099.78/11/2011 0:0005ANTIOQUIA5308.0GIRARDOTAFijaPOINT (-75.443986 6.378517)
29,020AMVAI.E. COL. COLOMBIA6.378517-75.443986PLiquidamm12,0114293.5741.000.02.812.82320/12/2011 2:001.507/11/2011 23:00:00 - 08/11/2011 05:00:00 - 13/11/2011 21:00:00 - 14/11/2011 20:00:00 - 18/11/2011 22:00:00 - 19/11/2011 03:00:00 - 21/11/2011 19:00:00 - 23/11/2011 19:00:00 - 23/11/2011 20:00:00 - 23/11/2011 21:00:00 - 24/11/2011 19:00:00 - 28/11/2011 20:00:00 - 30/11/2011 15:00:00 - 05/12/2011 03:00:00 - 05/12/2011 04:00:00 - 08/12/2011 03:00:00 - 13/12/2011 09:00:00 - 21/12/2011 05:00:00 - 23/12/2011 02:00:0005ANTIOQUIA5308.0GIRARDOTAFijaPOINT (-75.443986 6.378517)
39,020AMVAI.E. COL. COLOMBIA6.378517-75.443986PmmHg12,011645.92,917,013.9284,51652.000.0646.1648.1649.812/09/2011 10:00641.627/10/2011 17:0005ANTIOQUIA5308.0GIRARDOTAFijaPOINT (-75.443986 6.378517)
49,020AMVAI.E. COL. COLOMBIA6.378517-75.443986PmmHg242,011645.9121,434.6918852.000.0645.9647.2647.920/10/2011 0:0064427/10/2011 0:0005ANTIOQUIA5308.0GIRARDOTAFijaPOINT (-75.443986 6.378517)
59,020AMVAI.E. COL. COLOMBIA6.378517-75.443986PSTugm3242,011726,353.058469.000.073.4124.8155.72/12/201121.123/06/201105ANTIOQUIA5308.0GIRARDOTAFijaPOINT (-75.443986 6.378517)
69,020AMVAI.E. COL. COLOMBIA6.378517-75.443986RGlobalWm212,011158.9224,195.87151,41116.000.06.5777.3946.619/11/2011 13:00403/11/2011 01:00:00 - 03/11/2011 02:00:00 - 03/11/2011 03:00:00 - 03/11/2011 04:00:00 - 03/11/2011 05:00:00 - 03/11/2011 06:00:00 - 03/11/2011 20:00:00 - 03/11/2011 21:00:00 - 03/11/2011 22:00:00 - 03/11/2011 23:00:00 - 04/11/2011 00:00:00 - 04/11/2011 01:00:00 - 04/11/2011 02:00:00 - 04/11/2011 03:00:00 - 04/11/2011 04:00:00 - 04/11/2011 05:00:00 - 04/11/2011 06:00:00 - 04/11/2011 20:00:00 - 04/11/2011 21:00:00 - 04/11/2011 22:00:00 - 04/11/2011 23:00:00 - 05/11/2011 00:00:00 - 05/11/2011 01:00:00 - 05/11/2011 02:00:00 - 05/11/2011 03:00:00 - 05/11/2011 04:00:00 - 05/11/2011 05:00:00 - 05/11/2011 06:00:00 - 05/11/2011 20:00:00 - 05/11/2011 21:00:00 - 05/11/2011 22:00:00 - 05/11/2011 23:00:00 - 06/11/2011 00:00:00 - 06/11/2011 01:00:00 - 06/11/2011 02:00:00 - 06/11/2011 03:00:00 - 06/11/2011 04:00:00 - 06/11/2011 05:00:00 - 14/11/2011 20:00:00 - 14/11/2011 21:00:00 - 14/11/2011 22:00:00 - 14/11/2011 23:00:00 - 15/11/2011 00:00:00 - 15/11/2011 01:00:00 - 15/11/2011 02:00:00 - 15/11/2011 03:00:00 - 15/11/2011 04:00:00 - 15/11/2011 05:00:00 - 15/11/2011 06:00:00 - 15/12/2011 20:00:00 - 15/12/2011 21:00:00 - 15/12/2011 22:00:00 - 15/12/2011 23:00:00 - 16/12/2011 00:00:00 - 16/12/2011 01:00:00 - 16/12/2011 02:00:00 - 16/12/2011 03:00:00 - 16/12/2011 04:00:00 - 16/12/2011 05:00:00 - 16/12/2011 06:00:00 - 16/12/2011 07:00:00 - 16/12/2011 21:00:00 - 16/12/2011 22:00:00 - 16/12/2011 23:00:00 - 17/12/2011 00:00:00 - 17/12/2011 01:00:00 - 31/12/2011 00:00:00 - 31/12/2011 04:00:00 - 31/12/2011 05:00:0005ANTIOQUIA5308.0GIRARDOTAFijaPOINT (-75.443986 6.378517)
79,020AMVAI.E. COL. COLOMBIA6.378517-75.443986RGlobalWm2242,011158.99,375.375916.000.0157227.4233.421/11/2011 0:00105.827/12/2011 0:0005ANTIOQUIA5308.0GIRARDOTAFijaPOINT (-75.443986 6.378517)
89,020AMVAI.E. COL. COLOMBIA6.378517-75.443986TAire2Celsius12,01121.214,682.273476948.000.020.227.129.322/12/2011 16:0016.812/12/2011 8:0005ANTIOQUIA5308.0GIRARDOTAFijaPOINT (-75.443986 6.378517)
99,020AMVAI.E. COL. COLOMBIA6.378517-75.443986TAire2Celsius242,01121.2613.55298.000.021.222.62322/12/2011 0:0019.48/12/2011 0:0005ANTIOQUIA5308.0GIRARDOTAFijaPOINT (-75.443986 6.378517)
ID EstacionAutoridad AmbientalEstaciónLatitudLongitudVariableUnidadesTiempo de exposición (horas)AñoPromedioSumaNo. de datosRepresentatividad TemporalExcedencias limite actualPorcentaje excedencias limite actualMedianaPercentil 98MáximoFechas/horas del máximoMínimoFechas/horas del mínimoDías de excedenciasCódigo del DepartamentoNombre del DepartamentoCódigo del MunicipioNombre del MunicipioTipo de EstaciónUbicacion
2872231,876SDAMÓVIL FONTIBÓN4.667694-74.148861COugm312,023804.26,297,503.547,83189.000.00686.92,093.94,421.424/02/2023 7:0002023-01-31 14:00:00 - 2023-01-31 15:00:00 - 2023-01-31 16:00:00 - 2023-07-20 04:00:00 - 2023-11-27 00:00:00011Bogotá, D.C.11001.0Bogota, D.C.FijaPOINT (-74.148861 4.667694)
2872331,876SDAMÓVIL FONTIBÓN4.667694-74.148861COugm382,023802.66,232,977.57,76689.000.007711,452.12,159.724/02/2023 10:00156.431/01/2023 19:00011Bogotá, D.C.11001.0Bogota, D.C.FijaPOINT (-74.148861 4.667694)
2872431,876SDAMÓVIL FONTIBÓN4.667694-74.148861NO2ugm312,02342.7333,719.277,82389.000.0042.177.7115.55/04/2023 20:006.626/11/2023 12:00011Bogotá, D.C.11001.0Bogota, D.C.FijaPOINT (-74.148861 4.667694)
2872531,876SDAMÓVIL FONTIBÓN4.667694-74.148861O3ugm382,02320.2142,354.87,03980.000.0016.366.996.627/01/2023 18:0002023-05-27 06:00:00 - 2023-07-28 04:00:00 - 2023-07-28 05:00:00 - 2023-07-28 06:00:00011Bogotá, D.C.11001.0Bogota, D.C.FijaPOINT (-74.148861 4.667694)
2872631,876SDAMÓVIL FONTIBÓN4.667694-74.148861PM10ugm312,02353.6416,156.87,76689.0156920.2048.8129.928831/08/2023 14:003.117/04/2023 5:0024511Bogotá, D.C.11001.0Bogota, D.C.FijaPOINT (-74.148861 4.667694)
2872731,876SDAMÓVIL FONTIBÓN4.667694-74.148861PM10ugm3242,02353.517,484.6632790.04513.7653.190.9101.531/08/2023 0:0016.48/01/2023 0:004511Bogotá, D.C.11001.0Bogota, D.C.FijaPOINT (-74.148861 4.667694)
2872831,876SDAMÓVIL FONTIBÓN4.667694-74.148861PM2.5ugm312,02321.5164,994.37,66087.097812.7718.957.4106.828/09/2023 21:000.42023-10-29 12:00:00 - 2023-11-04 20:00:0021511Bogotá, D.C.11001.0Bogota, D.C.FijaPOINT (-74.148861 4.667694)
2872931,876SDAMÓVIL FONTIBÓN4.667694-74.148861PM2.5ugm3242,02321.66,918.8132188.0195.9220.941.250.130/11/2023 0:003.829/10/2023 0:001911Bogotá, D.C.11001.0Bogota, D.C.FijaPOINT (-74.148861 4.667694)
2873031,876SDAMÓVIL FONTIBÓN4.667694-74.148861SO2ugm312,0233.829,827.297,80189.000.002.419.384.614/10/2023 8:0002023-01-12 03:00:00 - 2023-01-12 06:00:00 - 2023-01-26 13:00:00 - 2023-01-26 14:00:00 - 2023-07-04 15:00:00 - 2023-07-21 13:00:00 - 2023-08-22 03:00:00 - 2023-08-22 04:00:00 - 2023-08-22 05:00:00 - 2023-08-22 06:00:00 - 2023-09-03 03:00:00 - 2023-09-03 04:00:00 - 2023-09-03 06:00:00 - 2023-09-23 06:00:00 - 2023-10-10 23:00:00 - 2023-10-11 00:00:00 - 2023-11-28 20:00:00 - 2023-11-28 21:00:00 - 2023-11-28 22:00:00 - 2023-11-28 23:00:00 - 2023-12-19 23:00:00 - 2023-12-20 00:00:00 - 2023-12-20 01:00:00011Bogotá, D.C.11001.0Bogota, D.C.FijaPOINT (-74.148861 4.667694)
2873131,876SDAMÓVIL FONTIBÓN4.667694-74.148861SO2ugm3242,0233.81,254.8732990.000.003.211.221.518/11/2023 0:000.522/08/2023 0:00011Bogotá, D.C.11001.0Bogota, D.C.FijaPOINT (-74.148861 4.667694)

Duplicate rows

Most frequently occurring

ID EstacionAutoridad AmbientalEstaciónLatitudLongitudVariableUnidadesTiempo de exposición (horas)AñoPromedioSumaNo. de datosRepresentatividad TemporalExcedencias limite actualPorcentaje excedencias limite actualMedianaPercentil 98MáximoFechas/horas del máximoMínimoFechas/horas del mínimoDías de excedenciasCódigo del DepartamentoNombre del DepartamentoCódigo del MunicipioNombre del MunicipioTipo de EstaciónUbicacion# duplicates
019,566CARCOLEGIO RAQUIRA5.537468-73.635NO2ugm3242,01124.83,547.6814379.000.0024.545.848.324/12/20116.421/01/2011015BOYACÁ15600.0RÁQUIRAFijaPOINT (-73.63499965 5.5374681)2
119,566CARCOLEGIO RAQUIRA5.537468-73.635NO2ugm3242,01234.64,984.7214479.000.0035.153.657.66/04/201213.631/01/2012015BOYACÁ15600.0RÁQUIRAFijaPOINT (-73.63499965 5.5374681)2
219,566CARCOLEGIO RAQUIRA5.537468-73.635NO2ugm3242,01413.61,267.159325.000.0013.514.1162/01/201412.820/11/2014015BOYACÁ15600.0RÁQUIRAFijaPOINT (-73.63499965 5.5374681)2
319,566CARCOLEGIO RAQUIRA5.537468-73.635NO2ugm3242,01514.14,584.1132589.000.0014.114.714.710/03/2015 - 22/04/2015 - 18/05/2015 - 27/08/2015 - 01/09/2015 - 03/09/2015 - 15/09/2015 - 16/09/2015 - 19/09/2015 - 12/12/2015 - 17/12/201513.312/11/2015015BOYACÁ15600.0RÁQUIRAFijaPOINT (-73.63499965 5.5374681)2
419,566CARCOLEGIO RAQUIRA5.537468-73.635NO2ugm3242,01613.6776.895716.000.0013.514.114.121/01/2016 - 11/02/201613.204/01/2016 - 16/01/2016 - 30/01/2016 - 14/02/2016015BOYACÁ15600.0RÁQUIRAFijaPOINT (-73.63499965 5.5374681)2
519,566CARCOLEGIO RAQUIRA5.537468-73.635PM10ugm312,016108.1797,172.48277,37684.000.009420685214/06/2016 2:002916/07/2016 14:00015BOYACÁ15600.0RÁQUIRAFijaPOINT (-73.63499965 5.5374681)2
619,566CARCOLEGIO RAQUIRA5.537468-73.635PM10ugm312,01746251,511.38595,46862.000.0045.8123.8199.48/09/2017 7:00010/06/2017 12:00:00 - 12/06/2017 01:00:00 - 01/07/2017 11:00:00 - 01/07/2017 15:00:00 - 02/07/2017 09:00:00015BOYACÁ15600.0RÁQUIRAFijaPOINT (-73.63499965 5.5374681)2
719,566CARCOLEGIO RAQUIRA5.537468-73.635PM10ugm312,01845.7313,821.37166,87078.05968.6832167.8447.315/06/2018 19:003.529/04/2018 1:009715BOYACÁ15600.0RÁQUIRAFijaPOINT (-73.63499965 5.5374681)2
819,566CARCOLEGIO RAQUIRA5.537468-73.635PM10ugm312,01949.8400,197.44898,03492.0154319.2138.1164.1361.68/10/2019 4:002.718/01/2019 08:00:00 - 01/06/2019 11:00:0025115BOYACÁ15600.0RÁQUIRAFijaPOINT (-73.63499965 5.5374681)2
919,566CARCOLEGIO RAQUIRA5.537468-73.635PM10ugm312,02050.3412,909.7158,20493.0164220.0139.3156.25907/02/2020 1:002.516/11/2020 12:0024015BOYACÁ15600.0RÁQUIRAFijaPOINT (-73.63499965 5.5374681)2